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Diagnostics
Special Issues
Diagnosis of Corneal and Retinal Diseases
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Diagnosis of Corneal and Retinal Diseases

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Clinical Diagnosis and Prognosis".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 1030

Special Issue Editors

Dr. Cristina Nicula

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Guest Editor
Department of Oral-Maxilo-Facial Surgery, Faculty of Medicine, “Iuliu Haţieganu” University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania
Interests: cataract extraction; eye diseases; cataract; dry eye syndromes; presbyopia; blepharitis; keratoconjunctivitis sicca; keratoconus
Special Issues, Collections and Topics in MDPI journals
Dr. Alina Popa-Cherecheanu

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Guest Editor
1. Department of Ophthalmology, Faculty of Medicine, Carol Davila University of Medicine and Pharmacy, 020021 Bucharest, Romania
2. Department of Ophthalmology, Emergency University Hospital, 050098 Bucharest, Romania
Interests: ophthalmology

Special Issue Information

Dear Colleagues,

A. The diagnostic tools employed to assess the cornea include slit-lamp examination, keratometry, and specialized imaging techniques; these enable ophthalmologists to assess the cornea's structures, curvature, and thickness, aiding in the diagnosis of various corneal diseases.

Specialized Corneal Diagnostic Tools:

  • Corneal Topography
  • Ultrasound Biomicroscopy (UBM)
  • Corneal Pachymetry
  • Anterior Segment OCT
  • Specular Microscopy
  • Confocal Microscopy
  • Epithelial Thickness Mapping (ETM)
  • Ocular Response Analyzer (ORA)

B. Retinal Diagnostic Methods:

The diagnostic methods utilized to examine the retina include a range of tools and techniques, from simple visual examinations to advanced imaging techniques; these enable the health of the retina to be assessed and various eye conditions to be detected, and help ophthalmologists diagnose and monitor retinal diseases such as diabetic retinopathy, macular degeneration, and retinal detachment.

Retinal diseases are assessed using various specialized diagnostic imaging  techniques, such as the following:

  1. Ophthalmoscopy
  2. Retinal Photography
  3. Fluorescein Angiography
  4. Indocyanine Green (ICG) Angiography
  5. Optical Coherence Tomography (OCT)
  6. Ophthalmic Ultrasound
  7. Amsler Grid Test
  8. Electroretinography (ERG)
  9. Electrophysiology
  10. Visual Field Testing
  11. Color Vision Testing
  12. Dark Adaptometry
  13. Angio-OCT
  14. Retinograph

15.Fundus autofluorescence (FAF)

  1. CT and MRI

By using these various diagnostic methods, ophthalmologists can accurately assess the health of the retina, diagnose and monitor various retinal conditions, and provide appropriate treatment plans.

We encourage authors to submit manuscripts that will enhance our knowledge regarding the diagnosis of corneal and retinal diseases.

Dr. Cristina Nicula
Dr. Alina Popa-Cherecheanu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • imaging diagnosis
  • corneal diseases
  • retinal diseases
  • optical coherence tomography (OCT)
  • ultrasound

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Published Papers (1 paper)

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Research

17 pages, 4792 KB  
Article
A Deep Learning-Based Graphical User Interface for Predicting Corneal Ectasia Scores from Raw Optical Coherence Tomography Data
by Maziar Mirsalehi and Achim Langenbucher
Diagnostics 2026, 16(2), 310; https://doi.org/10.3390/diagnostics16020310 - 18 Jan 2026
Viewed by 644
Abstract
Background/Objectives: Keratoconus, a condition in which the cornea becomes thinner and steeper, can cause visual problems, particularly when it is progressive. Early diagnosis is important for preserving visual acuity. Raw data, unlike preprocessed data, are unaffected by software modifications. They retain their [...] Read more.
Background/Objectives: Keratoconus, a condition in which the cornea becomes thinner and steeper, can cause visual problems, particularly when it is progressive. Early diagnosis is important for preserving visual acuity. Raw data, unlike preprocessed data, are unaffected by software modifications. They retain their native structure across versions, providing consistency for analytical purposes. The objective of this study was to design a deep learning-based graphical user interface for predicting the corneal ectasia score using raw optical coherence tomography data. Methods: The graphical user interface was developed using Tkinter, a Python library for building graphical user interfaces. The user is allowed to select raw data from the cornea/anterior segment optical coherence tomography Casia2, which is generated in the 3dv format, from the local system. To view the predicted corneal ectasia score, the user must determine whether the selected 3dv file corresponds to the left or right eye. Extracted optical coherence tomography images are cropped, resized to 224 × 224 pixels and processed by the modified EfficientNet-B0 convolutional neural network to predict the corneal ectasia score. The predicted corneal ectasia score value is displayed along with a diagnosis: ‘No detectable ectasia pattern’ or ‘Suspected ectasia’ or ‘Clinical ectasia’. Performance metric values were rounded to four decimal places, and the mean absolute error value was rounded to two decimal places. Results: The modified EfficientNet-B0 obtained a mean absolute error of 6.65 when evaluated on the test dataset. For the two-class classification, it achieved an accuracy of 87.96%, a sensitivity of 82.41%, a specificity of 96.69%, a positive predictive value of 97.52% and an F1 score of 89.33%. For the three-class classification, it attained a weighted-average F1 score of 84.95% and an overall accuracy of 84.75%. Conclusions: The graphical user interface outputs numerical ectasia scores, which improves other categorical labels. The graphical user interface enables consistent diagnostics, regardless of software updates, by using raw data from the Casia2. The successful use of raw optical coherence tomography data indicates the potential for raw optical coherence tomography data to be used, rather than preprocessed optical coherence tomography data, for diagnosing keratoconus. Full article
(This article belongs to the Special Issue Diagnosis of Corneal and Retinal Diseases)
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